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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Modeling and Classifying Hyperspectral Imagery by CRFs With Sparse Higher Order Potentials
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Modeling and Classifying Hyperspectral Imagery by CRFs With Sparse Higher Order Potentials

机译：具有稀疏高阶电势的CRF对高光谱图像进行建模和分类

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摘要

Hyperspectral images exhibit strong dependencies across spatial and spectral neighbors, which have been proved to be very useful for hyperspectral image classification. The recently defined conditional random field (CRF) can effectively model and use the dependencies for classification of hyperspectral images in a unified probabilistic framework. However, in order to be computationally tractable, the usual CRFs are limited to incorporate only pairwise potentials. Thus, the usual CRFs can capture only pairwise interactions and neglect higher order dependencies, which are potentially useful high-level properties particularly for the classification of hyperspectral image consisting of complex components. This paper overcomes this limitation by developing hyperspectral image classification algorithm based on a CRF with sparse higher order potentials, which are specially designed to incorporate complex characteristics of hyperspectral images. To efficiently implement the CRF model at training step, this paper develops an efficient local method under the piecewise training framework, while at inference step, this proposes a simple strategy to combine the piecewisely trained model to overcome the possible over-counting problems. Moreover, the combined model with the specially defined potentials can be efficiently inferred by graph cut method. Experiments on the real-world data attest to the accuracy, effectiveness, and efficiency of the proposed model on modeling and classifying hyperspectral images.
机译：高光谱图像在空间和光谱邻居之间表现出很强的依赖性，这已被证明对于高光谱图像分类非常有用。最近定义的条件随机场（CRF）可以在统一的概率框架中有效建模并使用相关性对高光谱图像进行分类。然而，为了易于计算，通常的CRF被限制为仅包含成对电位。因此，通常的CRF只能捕获成对的交互作用，而忽略更高阶的依存关系，这对于潜在的有用的高级属性尤其是对于由复杂成分组成的高光谱图像的分类而言。本文通过开发基于稀疏高阶电势的CRF的高光谱图像分类算法克服了这一局限，该算法专门设计用于合并高光谱图像的复杂特征。为了在训练步骤中有效地实现CRF模型，本文在分段训练框架下开发了一种有效的局部方法，而在推理步骤中，这提出了一种简单的策略来组合分段训练模型，以克服可能出现的计数过多问题。此外，可以通过图割法有效地推断具有特定定义的电势的组合模型。实际数据的实验证明了该模型在对高光谱图像进行建模和分类时的准确性，有效性和效率。

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